Within the project, we have performed numerical simulations of binary neutron star mergers, covering a wide range of system parameters such as masses, spins, and orbital eccentricities. In addition, we continuously increase the realism of the microphysical treatment, e.g by incorporating realistic physical effects including magnetic fields, neutrino radiation (with novel inclusion of muonic neutrinos), and advanced neutron star equations of state to represent matter under extreme conditions better. By comparing different numerical codes, we identified areas where waveform models require improvement, particularly for highly spinning systems. Machine-learning techniques were developed to accelerate key computational steps and to predict merger outcomes, which supports the modeling of postmerger phases.
Significant progress was made in developing and refining gravitational-wave models that describe tidal interactions of the binary neutron star coalescence, including also higher-order modes in the signals. While all these models were based on numerical-relativity simulations, we have also started to investigate how observational data can be used to calibrate waveform models. To check this ansatz, we have performed a study based on mock observational data, with a view toward future detectors such as the Einstein Telescope.
Since neutron star merger do not only create gravitational waves, we also investigated possible electromagnetic signatures of neutron star mergers. In this regard, we improved numerical tools to model non-thermal emissions from gamma-ray bursts and kilonova afterglows, enabling more precise extraction of astrophysical parameters from observations. Real-time frameworks for transient lightcurve fitting were also advanced to support rapid analysis of new events.
A key achievement was the creation of a unified multi-messenger analysis framework that integrates gravitational-wave data, electromagnetic observations, and nuclear physics constraints. This framework allows for a comprehensive interpretation of neutron star merger signals and is being upgraded to leverage GPU computing for improved efficiency.
Together, our efforts have resulted in a suite of high-accuracy simulations, models, and data-analysis tools that advance our understanding of neutron star mergers, nuclear matter at supranuclear densities, and the expansion rate of the Universe. Beyond the core scientific outcomes, the project fostered interdisciplinary collaborations with nuclear physicists and expanded international scientific networks through dedicated workshops and research visits, enriching the broader astrophysics community.
